Alright guys, first of all: Thank you so much for making this available for free. I will start today, 20.11.2024 and will give you a review of this course. It's been a long ride to be able to grasp what you're talking about. Wish me good luck.
When someone explains you the meaning of the math used in physics or any science, the subject becomes doubly interesting. I am sure this lecture series will fit to that category. Thanks to the creators of the course and looking forward to the journey of this lecture series.
The secret is to understand is sometimes to ignore some things....Deal on what we have now and improve on it. Binary in quantum computer does not going to go away for sometime. It only describe the state of ONE ATOM versus a series of atoms describe to produce an effect of a "GATE."
I think this series can easily become a cult in the quantum information education space: concise and simple to digest, even when the topic is elusive for the classically formed brain. Thank you IBM for making quantum this accesible.
Before adding a comment, decided to read a few others below. As it happens, they ALL say exactly what I wanted to say. Every time I come back, especially when I pause the video and actually "do the math(s)", I "get" something new that was only vaguely (or more likely, not to any degree at all) understood. Such a privilege to have this freely available wealth of the real kind of deep learning to digest, each at our own pace and foundation background (or even lack thereof). Thank you for being such a clear and cogent guide to us grasshoppers. Long live the Copenhagen Interpretation!
I'm a quantum entusiast for some time now, I've seen a couple attempts to explain quantum in simple but very precise terms. This is by far the best explanation I've seen. It's step by step, no skipping because something will be covered later or is too difficult, which made me have multiple "aha!" moments. Thank you very much for this and kudos to Mr Watrous!
Really wonderful explanation of the basis of quantum managing information and operations. John Watrous is very clear in his words and in examples shown. Thank you to all the Qiskit team for releasing this educational jewel for free.
What a terrific lesson! His explanations are clear and complete. He does not leave me wondering how conclusions are drawn because he lays it all out so clearly. I might have thought that this would make the lesson tedious, but the opposite is true. Because the lesson is so clear, the material just flows, and an hour and 10 minutes is over in no time at all. Finally, he does make a few comments about deeper things that he does not prove. But those will be covered in future lessons, he assures us, or with a little extra effort, I can discover them on my own. In this way, he gives little victories to his audience. Quite remarkable, thanks.
This is awesome! How he begins with Classical Information and smoothly guides into Quantum System Information, explaining key Quantum information concepts along the way, is very beautifully done. It is very easy to understand and digest. Thank you so much for teaching this in a very clear and concise manner. Thank you, John!
The mathematics is usually put aside or ignored when this topic is popularly discussed - so correcting this omission with this series will prove to be extremely interesting and important. Can't wait for more.
When talking about the Euclidean norm I think it's helpful to mention that when we multiply the coefficient with it's complex conjugate. For example in the (1+2i)/3 |0> - 2/3 |1> so "the absolute value squared" mentioned means that we would take (1+2i)/3 * (1-2i)/3 = ( 1^2 - (2i)^2 )/9 = 5/9 I'm just putting this comment here incase it's helpful to anyone trying to figure out how to get 5/9 😃 Thank you for putting this content out! It's very well done!!💥👌
I appreciate this in depth response to what I've questioned for a while and sought a thorough and concise explanation of. You being the only educator who I've found that has offered straightforward, complete, concise and clear information on this topic. your ability to conceptualize and clarify the essential necessary knowledge base without attempting to over simplify it is refreshing and helpful. Grateful and thankful for sharing your knowledge on the matter to all interested. Grateful 🙏👌
I have so much respect for this man here. He was able to teach for an hour straight with crystal clear information. Not sure if he had a teleprompter though
Thank you,honestly speaking such an excellent basic approach is not followed by other courses ,this one hour video will save people days of not having understood more complex theoretical concepts in the future.
There is a funny convention about firsts and the number one: usually we associate them with each other. "1" reprents the first of a series, and the first of a series is identified with the digit "1." I suppose this is some sort of daring innovation by IBM here, using the numbering "01" to identify the second in a series and telling us to go find the first video by ourselves. Congratulations, IBM! It's wonderful to see this pioneering spirit here in UA-cam.
🎯 Key Takeaways for quick navigation: 00:00 🎓 The video series aims to provide a comprehensive understanding of quantum information and computation, focusing on the technical details of quantum information and its applications. 01:02 🔬 Lesson one focuses on quantum information for single systems, laying the foundation for understanding quantum information for multiple systems and quantum algorithms. 02:01 🔄 Classical information serves as a starting point to understand quantum information, with quantum information being an extension of classical information. 03:41 🧪 There are two descriptions of quantum information: simplified and general. The simplified description focuses on vectors and unitary matrices, while the general description is more powerful, including density matrices and noise modeling. 08:02 📊 Classical states are configurations that can be unambiguously described, and they are represented by a finite set called sigma. 11:41 🧪 The Dirac notation is introduced to describe vectors, using "ket" notation for classical states and standard basis vectors. 15:51 🔍 Measuring a system in a probabilistic state results in knowledge of the classical state with probabilities transitioning to certainty (probability 1) for the observed state. 19:30 🔄 Deterministic operations on classical systems are described by functions and corresponding matrices, where the output depends entirely on the input classical state. Matrix-vector multiplication can represent the effect of deterministic operations on probabilistic states. 24:35 🧮 Quantum information can be represented using column vectors called quantum state vectors with complex number entries, and the Euclidean norm of these vectors must equal one. 27:12 🔄 The inner product, or bracket, of a bra vector and a ket vector is an important concept in quantum information, and it represents the multiplication of a row vector and a column vector. 31:18 🎲 Probabilistic operations in quantum information can introduce randomness or uncertainty, and they are represented by stochastic matrices, which are matrices with non-negative real entries that sum to one in each column. 35:41 🔄 Composing probabilistic operations in quantum information is done by multiplying the corresponding stochastic matrices in the reverse order, and the order of operations matters. 40:03 🌌 Quantum states are represented by quantum state vectors, which are column vectors with complex number entries, and their Euclidean norm must be equal to one, making them unit vectors. 46:35 🃏 Quantum state vectors can represent quantum states of various systems, not just qubits, and they satisfy the condition that the sum of the absolute values squared of their entries equals one. 48:16 🧬 Dirac notation can be used for arbitrary vectors in quantum physics. Kets represent column vectors, and bras represent row vectors. Any name can be used inside a bra or ket to refer to a vector. 49:18 🧩 When using Dirac notation for arbitrary vectors, the bra vector is the conjugate transpose of the corresponding ket vector. This involves transposing the vector and taking the complex conjugate of each entry. 51:22 📊 Measurements in quantum systems provide a way to extract classical information from quantum states. Standard basis measurements are the simplest and most basic type of measurement. 52:28 📈 The outcomes of a measurement in quantum systems are classical states, and each outcome has a probability associated with it. The probabilities are the absolute value squared of the entries in the quantum state vector. 55:08 🌌 When a quantum system is measured, its state may change, and the new state will be the one corresponding to the classical outcome of the measurement. 56:13 🔀 Unitary operations in quantum physics are represented by unitary matrices. These matrices describe how quantum states of systems can be changed. Unitary operations preserve the Euclidean norm of quantum state vectors. 59:28 ⚙️ Compositions of unitary operations are represented by matrix multiplication, and the order of multiplication is from right to left. Unitary matrices are closed under multiplication, resulting in another unitary matrix. 01:09:36 🔄 The combination of Hadamard, S, and Hadamard operations gives rise to a square root of NOT operation, which is an example of how quantum operations behave differently from classical operations.
Thank you so much for this lesson! It was very clear, simple and easy to understand, but not too easy. Requires some basic mathematics knowledge, but I loved it. Seriously, kudos to Mr. Watrous (& Qiskit of course) for making this video:))
Dr. Watrous, just wanted to say I really appreciate the note you added (starting at 47:43) about the symbols being put inside ket being ambiguous (not always depicting a classical state). This is something not mentioned at all generally and something that always confused me whenever I read any QC material.
I highly recommend Needham's book on _Visual Differential Geometry and Forms._ There we find that Dirac's bras are _1-forms,_ which come to us from the mathematician Hodge, whose work was inspired by Maxwell's equations. (Chapter 32 3.5) I have often argued that we need to have a closer look at electromagnetism (EM) in order to get on with machine vision. Hodge shows us the way.
When i was a collage student i was into maxwell's studies and at the same time studying dirac's theorems and i wondered if they are related in some kinda way, so that was the FACT. Thank you so much Brian!! Also, if Maxwell himself would live 10-25 years longer, he could also define quantum physics with his hands way years before in my opinion..
Discovered just today this very interesting introductory lesson: I think I will follow the entire course together with the reading of the M&M's book. Great! Many thanks to Qiskit.
John thank you very much for taking the time to explain such a complex topic in such an understandable way. I really appreciate your hard work and dedication.
At 24:00, the matrix maps the 'ket a' to another 'ket b' = 'ket, f(a)', which is either equal to or not equal to 'ket a'. So, the table is comparing the result of the operation on two different possible 'ket a' states, either 'ket 0' or 'ket 1'. I was quite confused by this at first, thinking that the columns in these charts were elements of the vector corresponding to 'ket a', which is not the case.
Yes. I'm glad it's not just me who found this (very) confusing! |a> has two possible values, [0,1] and [1,0] (transposed). We need to multiply each of these 2 values by a matrix to see what the matrix is acually doing. This is omitted from the explanation. I don't know if anyne else has commented on this but it's not just a 'cosmetic' problem if you are studying this subject for the first time.
Great Lesson! @John Watrous around 59:00 I did not get the: "The stochastic matrices are precisely the matrices that always transform probability vectors into probability vectors"
@@yukihirasoma3935 They are correct as they appear. If you could explain why *you* think the transposes would be right I would be interested to hear that, because I'd like to understand where the confusion lies. My explanation for why the transpose doesn't work for M_1 is to consider the action of the transpose on the vectors |0> and |1>. We have M_1^T |0> = |0> + |1> (which isn't a probabilistic state because the "probabilities" sum to 2) and M_1^T |1> = 0 (not |0> but 0 - the zero vector - which is also not a probabilistic state). Similar for M_4 - and of course M_2 and M_3 are symmetric so we agree on them. To be clear, I'm talking about multiplying a column vector by a matrix from the left. Could it be that you're thinking about multiplying a row vector by a matrix from the right?
The definition of classical information for an event "e", is -log(P[e]). Please explain in other next video the information of quantum state |e> or desnity matrix Σ{|e>
32:51 I realized it's easier to understand the operations by looking at the matrix as a transformer, with the input from the column vector with first column as input 0 and second column as input 1 and output as the row vectors, with first row as output 0 and second row as output 1.
The characteristic of an n-dimensional manifold is that each of the elements composing it (in our examples, single points, conditions of a gas, colors, tones) may be specified by the giving of _n_ quantities, the "coordinates," which are continuous functions within the manifold. ~Weyl We express the fact that _n_ parameters are necessary and sufficient for a unique characterization of the configuration of the system by saying that it has "_n_ degrees of freedom." ~Lanczos
@yes, I see that now. What helped me was realizing the matrix was representing the function not changing the outputs. The matrix was mapping the output and that column(1) represents a=0, and the row(1) b=0, row(2) b=1. The 1 in the matrix is just indicated the index of the desired output per input, the matrix could have X’s instead of 1’s and would still be the same. Thank you for the explanation!
For those who needs to manipulate (Like me) and before using Qiskit (Which I intend) you may use Python sympy, create matrices ket, H, S gate, T gate, yes with complex number, and so on and play with them. Sure it's good to do it on paper but with symbolic tool it is fun too. And you may verify if you were correct on paper too ... There is also a book Qiskit / IBM which I ordered. Since I'd like to play a little wit a real quantum computer.
I have a question about the quantum information part, when you have a vector ket something, how do you know if it is a standard basis vector or a column vector in general, do you have to understand that based on how it's used?
M(b,a) means the entry in the bth row and ath column. So the first entry in the matrix is M(0,0), which is the entry in the upper left corner of the matrix. The second (to the right of the first entry) is M(0,1) and so on. The matrix for each of the function( f1, f2, f3, f4) can be constructed using the formula M(b,a) = 1 if b = f(a) and 0 if b is not equal to f(a). For example, consider the first function and its matrix ( M1 ) use the formula to find the first entry i.e M(0,0). Comparing with M(b,a), here b = 0 and a = 0. So, check if b = f(a). 0 = f(0) which is true hence the M(0,0) entry is 1 according to the formula. Consider the third entry (lower left corner) M(1,0) here, b = 1 and a = 0, now the formula b is not equal to f(a) applies hence the entry is zero ( since b = 1 and f(a)=f(0)= 0, so b is not equal to f(a) ). All the other matrices can be obtained similarly.
at minute 42:18 it is stated that they are the sum of the absolute values..... but they are not absolute values, the ak are complex numbers.... so they should be amplitudes. Moreover it would not make sense to square an absolute value.... Is that so?
The absolute value of a complex number is a well-defined quantity and this terminology is standard. (Magnitude and modulus are also standard terms that mean the same thing in this context.) You can definitely square the absolute value of a complex number - but in practice it's generally simplest to compute the absolute value squared by multiplying the complex number to its conjugate (and then the absolute value without the square is the square root of the number you get, which will always be a nonnegative real number). See en.wikipedia.org/wiki/Complex_number#Complex_conjugate,_absolute_value_and_argument for more information.
@@zorkan Don't worry about it, I don't care about titles! If I was going to make a correction I would say that thanks are also due to the amazing video team that makes these videos possible.
This is a very beautiful and informative series for a general reader. I have a small suggestion for Prof. John: if he can upload the detailed PDF of every lecture as if he has added the detailed text explanation, then it will be more helpful to everybody. Because everyone can take the printout and read it. @Qiskit
Of course, with matrices describing operations, you can map every input to every output. But the downside is, that they usually get very big. Especially when Tensors-/Kroneckerproduct come into play. Without tools like numpy, you're completely lost.
It's like watching a new baby being born. It's a real exciting time to be alive, to witness and to be apart of the birth of this new quantum technology. SOSSTSE SCIENTIFIC TECHNOLOGY SOLUTIONS. ❤❤❤🎉🎉🎉
Like ket 0...ket 1 shouldn't be read like that ket 1 is the vector that has zero in the entry corresponding to the classical state one, which is the first entry and a 1 for all other entries?
I understood the topics discussed in the video. But when I moved to the Qiskit examples in the reading, I didn't understand anything. Is it because I am lacking in the topics discussed in the video or is it because of my unfamiliarity with the Numpy? Do you think I should rewatch the entire video again?
Wow! All very cool, man if only Qiskit were to release all videos in a unit one month apart, with a short break between units. Now that would be something else! 😅
Top tier education like this being free is not a given, I want to thank you from the bottom of my heart as this is amazing!
Correct
Alright guys, first of all: Thank you so much for making this available for free.
I will start today, 20.11.2024 and will give you a review of this course. It's been a long ride to be able to grasp what you're talking about.
Wish me good luck.
When someone explains you the meaning of the math used in physics or any science, the subject becomes doubly interesting. I am sure this lecture series will fit to that category.
Thanks to the creators of the course and looking forward to the journey of this lecture series.
The secret is to understand is sometimes to ignore some things....Deal on what we have now and improve on it. Binary in quantum computer does not going to go away for sometime. It only describe the state of ONE ATOM versus a series of atoms describe to produce an effect of a "GATE."
I think this series can easily become a cult in the quantum information education space: concise and simple to digest, even when the topic is elusive for the classically formed brain. Thank you IBM for making quantum this accesible.
Finally the rigorous approach we needed and with beautiful animations.Thank you Qiskit.
Before adding a comment, decided to read a few others below. As it happens, they ALL say exactly what I wanted to say. Every time I come back, especially when I pause the video and actually "do the math(s)", I "get" something new that was only vaguely (or more likely, not to any degree at all) understood. Such a privilege to have this freely available wealth of the real kind of deep learning to digest, each at our own pace and foundation background (or even lack thereof). Thank you for being such a clear and cogent guide to us grasshoppers. Long live the Copenhagen Interpretation!
I'm a quantum entusiast for some time now, I've seen a couple attempts to explain quantum in simple but very precise terms.
This is by far the best explanation I've seen. It's step by step, no skipping because something will be covered later or is too difficult, which made me have multiple "aha!" moments.
Thank you very much for this and kudos to Mr Watrous!
This is a talented teacher and a very effective way of explaining this topic. Thank you for putting this on UA-cam for all of us to benefit. 🎸
He has such a soothing voice and elucidates concepts in such a lucid manner!
Really wonderful explanation of the basis of quantum managing information and operations. John Watrous is very clear in his words and in examples shown.
Thank you to all the Qiskit team for releasing this educational jewel for free.
What a terrific lesson! His explanations are clear and complete. He does not leave me wondering how conclusions are drawn because he lays it all out so clearly. I might have thought that this would make the lesson tedious, but the opposite is true. Because the lesson is so clear, the material just flows, and an hour and 10 minutes is over in no time at all. Finally, he does make a few comments about deeper things that he does not prove. But those will be covered in future lessons, he assures us, or with a little extra effort, I can discover them on my own. In this way, he gives little victories to his audience. Quite remarkable, thanks.
This is awesome! How he begins with Classical Information and smoothly guides into Quantum System Information, explaining key Quantum information concepts along the way, is very beautifully done. It is very easy to understand and digest. Thank you so much for teaching this in a very clear and concise manner. Thank you, John!
You sort of explained here in 2 minutes what I couldn't understand after hours and hours of the MIT course. Thank you!
The mathematics is usually put aside or ignored when this topic is popularly discussed - so correcting this omission with this series will prove to be extremely interesting and important. Can't wait for more.
Oh Yes!
When talking about the Euclidean norm I think it's helpful to mention that when we multiply the coefficient with it's complex conjugate. For example in the (1+2i)/3 |0> - 2/3 |1> so "the absolute value squared" mentioned means that we would take (1+2i)/3 * (1-2i)/3 = ( 1^2 - (2i)^2 )/9 = 5/9
I'm just putting this comment here incase it's helpful to anyone trying to figure out how to get 5/9 😃
Thank you for putting this content out! It's very well done!!💥👌
That's Helps. Thanks
Thank you so much, I was confused here.
Thank you so much!!
Thanks a lot! I was trying to do (1+2i)^2.. you saved my time:)
I appreciate this in depth response to what I've questioned for a while and sought a thorough and concise explanation of.
You being the only educator who I've found that has offered straightforward, complete, concise and clear information on this topic. your ability to conceptualize and clarify the essential necessary knowledge base without attempting to over simplify it is refreshing and helpful. Grateful and thankful for sharing your knowledge on the matter to all interested.
Grateful 🙏👌
I have so much respect for this man here.
He was able to teach for an hour straight with crystal clear information. Not sure if he had a teleprompter though
maybe
One of best explanation by sir !! It's people like prof John Watrous who explains these complex topic in a easy and precise way thank you qiskit
You are a genius!!! I've always been frustrated at the notation part, but you made me to proceed to the next step! Thank you so much
I've been longing for a in depth explanation of Quantum Computing and you provided it flawlessly. I'm excited for the next installment!
One of the best explanations so far. Kudos to Prof. John Watrous for breaking the course down to dummies 👍
First 5 minutes of nothing
Thank you,honestly speaking such an excellent basic approach is not followed by other courses ,this one hour video will save people days of not having understood more complex theoretical concepts in the future.
There is a funny convention about firsts and the number one: usually we associate them with each other. "1" reprents the first of a series, and the first of a series is identified with the digit "1."
I suppose this is some sort of daring innovation by IBM here, using the numbering "01" to identify the second in a series and telling us to go find the first video by ourselves.
Congratulations, IBM! It's wonderful to see this pioneering spirit here in UA-cam.
This is the first lesson of the series...
🎯 Key Takeaways for quick navigation:
00:00 🎓 The video series aims to provide a comprehensive understanding of quantum information and computation, focusing on the technical details of quantum information and its applications.
01:02 🔬 Lesson one focuses on quantum information for single systems, laying the foundation for understanding quantum information for multiple systems and quantum algorithms.
02:01 🔄 Classical information serves as a starting point to understand quantum information, with quantum information being an extension of classical information.
03:41 🧪 There are two descriptions of quantum information: simplified and general. The simplified description focuses on vectors and unitary matrices, while the general description is more powerful, including density matrices and noise modeling.
08:02 📊 Classical states are configurations that can be unambiguously described, and they are represented by a finite set called sigma.
11:41 🧪 The Dirac notation is introduced to describe vectors, using "ket" notation for classical states and standard basis vectors.
15:51 🔍 Measuring a system in a probabilistic state results in knowledge of the classical state with probabilities transitioning to certainty (probability 1) for the observed state.
19:30 🔄 Deterministic operations on classical systems are described by functions and corresponding matrices, where the output depends entirely on the input classical state. Matrix-vector multiplication can represent the effect of deterministic operations on probabilistic states.
24:35 🧮 Quantum information can be represented using column vectors called quantum state vectors with complex number entries, and the Euclidean norm of these vectors must equal one.
27:12 🔄 The inner product, or bracket, of a bra vector and a ket vector is an important concept in quantum information, and it represents the multiplication of a row vector and a column vector.
31:18 🎲 Probabilistic operations in quantum information can introduce randomness or uncertainty, and they are represented by stochastic matrices, which are matrices with non-negative real entries that sum to one in each column.
35:41 🔄 Composing probabilistic operations in quantum information is done by multiplying the corresponding stochastic matrices in the reverse order, and the order of operations matters.
40:03 🌌 Quantum states are represented by quantum state vectors, which are column vectors with complex number entries, and their Euclidean norm must be equal to one, making them unit vectors.
46:35 🃏 Quantum state vectors can represent quantum states of various systems, not just qubits, and they satisfy the condition that the sum of the absolute values squared of their entries equals one.
48:16 🧬 Dirac notation can be used for arbitrary vectors in quantum physics. Kets represent column vectors, and bras represent row vectors. Any name can be used inside a bra or ket to refer to a vector.
49:18 🧩 When using Dirac notation for arbitrary vectors, the bra vector is the conjugate transpose of the corresponding ket vector. This involves transposing the vector and taking the complex conjugate of each entry.
51:22 📊 Measurements in quantum systems provide a way to extract classical information from quantum states. Standard basis measurements are the simplest and most basic type of measurement.
52:28 📈 The outcomes of a measurement in quantum systems are classical states, and each outcome has a probability associated with it. The probabilities are the absolute value squared of the entries in the quantum state vector.
55:08 🌌 When a quantum system is measured, its state may change, and the new state will be the one corresponding to the classical outcome of the measurement.
56:13 🔀 Unitary operations in quantum physics are represented by unitary matrices. These matrices describe how quantum states of systems can be changed. Unitary operations preserve the Euclidean norm of quantum state vectors.
59:28 ⚙️ Compositions of unitary operations are represented by matrix multiplication, and the order of multiplication is from right to left. Unitary matrices are closed under multiplication, resulting in another unitary matrix.
01:09:36 🔄 The combination of Hadamard, S, and Hadamard operations gives rise to a square root of NOT operation, which is an example of how quantum operations behave differently from classical operations.
Thank you so much for this lesson! It was very clear, simple and easy to understand, but not too easy. Requires some basic mathematics knowledge, but I loved it. Seriously, kudos to Mr. Watrous (& Qiskit of course) for making this video:))
Thanks a lot IBM, qiskit and professor Watrous for this amazing course!
Such a great teacher. Perfect pace and explains everything completely without overdoing it. I really appreciate the scaffolding for future topics.
Dr. Watrous, just wanted to say I really appreciate the note you added (starting at 47:43) about the symbols being put inside ket being ambiguous (not always depicting a classical state). This is something not mentioned at all generally and something that always confused me whenever I read any QC material.
I highly recommend Needham's book on _Visual Differential Geometry and Forms._
There we find that Dirac's bras are _1-forms,_ which come to us from the mathematician Hodge, whose work was inspired by Maxwell's equations. (Chapter 32 3.5)
I have often argued that we need to have a closer look at electromagnetism (EM) in order to get on with machine vision. Hodge shows us the way.
When i was a collage student i was into maxwell's studies and at the same time studying dirac's theorems and i wondered if they are related in some kinda way, so that was the FACT. Thank you so much Brian!! Also, if Maxwell himself would live 10-25 years longer, he could also define quantum physics with his hands way years before in my opinion..
Discovered just today this very interesting introductory lesson: I think I will follow the entire course together with the reading of the M&M's book. Great! Many thanks to Qiskit.
Amazing lecture. So clear and engaging. Thank you!
great videos, it has really helped me understand how quantum circuits are developed and how they do what they do. thanks.
This is masterfully taught. Absolutely awesome course.
Fantastic! I always wanted to know why we had to use unitary matrix and you finally explained it !
John thank you very much for taking the time to explain such a complex topic in such an understandable way. I really appreciate your hard work and dedication.
A very good lecture. Hope to watch lecture 2 ASAP. Thanks to Prof. John Watrous giving sixh an interesting lectures.
Amazing lecture and a crystal clear knowledge. I am very happy I discovered Qiskit. And now I need more. :)
timelines to focus:
24:57
29:48
42:20
46:03
54:27
58:30
01:00:16
I am finding it super helpful to watch this lecture in parts, and then reading up the associated Qiskit textbook sections.
Thank you for all the videos always very interesting and well explained!
At 24:00, the matrix maps the 'ket a' to another 'ket b' = 'ket, f(a)', which is either equal to or not equal to 'ket a'. So, the table is comparing the result of the operation on two different possible 'ket a' states, either 'ket 0' or 'ket 1'. I was quite confused by this at first, thinking that the columns in these charts were elements of the vector corresponding to 'ket a', which is not the case.
Yes. I'm glad it's not just me who found this (very) confusing! |a> has two possible values, [0,1] and [1,0] (transposed). We need to multiply each of these 2 values by a matrix to see what the matrix is acually doing. This is omitted from the explanation. I don't know if anyne else has commented on this but it's not just a 'cosmetic' problem if you are studying this subject for the first time.
Great Lesson! @John Watrous around 59:00 I did not get the: "The stochastic matrices are precisely the matrices that always transform probability vectors into probability vectors"
I think at 47:40 the state can't be a qstate, simply because the Euclidean norm equals half not one.
That's not correct. The Euclidean norm of this vector is in fact equal to 1.
@@John.WatrousSir but isn't (1/2)^2+(i/2)^2+(1/√2)^2 = 1/4 + (-1/4) + (1/2) = 1/2? Please let me know where am I going wrong
@@arnavmishra2155 Don't forget to take the absolute values.
Remember that the absolute square of a complex number is calculated by multiplying it by its conjugate. In the case above |1i/2|^2=1/4•-(i^2)=1/4.
smart speaker with a lucid crisp and simple ecture. thank you. God obless
Thank you for this! And very helpful that you are explaining complex details with clear and great examples.
thanks a lot to creators for giving such relevant content
This is an amazing introduction for me and it came at the right time since I'm taking a quantum computing course this winter
Excellent Lecture, I am mind blown. Looking forward to build up!
Eagerly looking forward to Lesson 2
Since today available ;)
thank you for the beautiful explanation of a complicated topic. I'm new to quantum computing and this material helps a lot!
Perfect mathematical intro to QC at the very basic level! Thanks a lot 👍
Was the matrix described @24:02 correct on the screen? Didn't seem to tie up with what was being said.
M1 and M4 don't seem correct
Their corresponding transposes would do the job
@@yukihirasoma3935 They are correct as they appear. If you could explain why *you* think the transposes would be right I would be interested to hear that, because I'd like to understand where the confusion lies. My explanation for why the transpose doesn't work for M_1 is to consider the action of the transpose on the vectors |0> and |1>. We have M_1^T |0> = |0> + |1> (which isn't a probabilistic state because the "probabilities" sum to 2) and M_1^T |1> = 0 (not |0> but 0 - the zero vector - which is also not a probabilistic state). Similar for M_4 - and of course M_2 and M_3 are symmetric so we agree on them. To be clear, I'm talking about multiplying a column vector by a matrix from the left. Could it be that you're thinking about multiplying a row vector by a matrix from the right?
Thanks for explaining, Dr. Watrous!
I understand now. Greatly appreciated.
Nicely presented. Looking forward to the series. Thank you!
IBM literally want to lead the technology and education videos like this has to inspire other companies
Great lecture!. But beginners may have to check the link above to explore more at their own pace.
The definition of classical information for an event "e", is -log(P[e]). Please explain in other next video the information of quantum state |e> or desnity matrix Σ{|e>
Plan to clear it. The exam looks tough. I was told this is the ultimate.
Thank you so much Qiskit, Mr. John Watrous
Thank you for this very clear and easy to folllow exposition.
Excellent explanations, clear and simple. Thank you.
32:51 I realized it's easier to understand the operations by looking at the matrix as a transformer, with the input from the column vector with first column as input 0 and second column as input 1 and output as the row vectors, with first row as output 0 and second row as output 1.
These lectures are great and valuable. Thx a lot!
23:22 I just cant get it? where can find more information to understand how this matrices came out?
sir, you are making history.
55:20 this is exceptionally important
Awesome and extremely professional!
The characteristic of an n-dimensional manifold is that each of the elements composing it (in our examples, single points, conditions of a gas, colors, tones) may be specified by the giving of _n_ quantities, the "coordinates," which are continuous functions within the manifold.
~Weyl
We express the fact that _n_ parameters are necessary and sufficient for a unique characterization of the configuration of the system by saying that it has "_n_ degrees of freedom."
~Lanczos
It was an excellent and detailed lecture. Thank you very much!
Very good explanation, for sure I will continue. Thank youu
Thank you very much. I am looking forward to the next lessons
At 23:34 how does matrix vector multiplication get us 00 for constant 0 function? I do M1*[01] column vector and get [10] for f(a)
The constant zero function(M1) doesn't turn each vector into 00 but rather turns it to ket 0, which is 10.
@yes, I see that now. What helped me was realizing the matrix was representing the function not changing the outputs. The matrix was mapping the output and that column(1) represents a=0, and the row(1) b=0, row(2) b=1. The 1 in the matrix is just indicated the index of the desired output per input, the matrix could have X’s instead of 1’s and would still be the same. Thank you for the explanation!
Nice. Looking forward to the next lessons.
EXACTLY what I needed, thank you so much
For those who needs to manipulate (Like me) and before using Qiskit (Which I intend) you may use Python sympy, create matrices ket, H, S gate, T gate, yes with complex number, and so on and play with them. Sure it's good to do it on paper but with symbolic tool it is fun too. And you may verify if you were correct on paper too ... There is also a book Qiskit / IBM which I ordered. Since I'd like to play a little wit a real quantum computer.
Muchas Gracias señor John Watrous.
I have a question about the quantum information part, when you have a vector ket something, how do you know if it is a standard basis vector or a column vector in general, do you have to understand that based on how it's used?
Yes, that's pretty much it... if you have a symbol inside of a ket you need to interpret that symbol to know what it means.
Plain and simplified, Thank you Qiskat 😁
Fantastic... really fantastic lesson !!!
AT 24:12 , I still don't understand how could we get M1, M2, M3 and M4 . Can someone explain it explicitly step by step operation ?
M(b,a) means the entry in the bth row and ath column. So the first entry in the matrix is M(0,0), which is the entry in the upper left corner of the matrix. The second (to the right of the first entry) is M(0,1) and so on. The matrix for each of the function( f1, f2, f3, f4) can be constructed using the formula M(b,a) = 1 if b = f(a) and 0 if b is not equal to f(a). For example, consider the first function and its matrix ( M1 ) use the formula to find the first entry i.e M(0,0). Comparing with M(b,a), here b = 0 and a = 0. So, check if b = f(a). 0 = f(0) which is true hence the M(0,0) entry is 1 according to the formula. Consider the third entry (lower left corner) M(1,0) here, b = 1 and a = 0, now the formula b is not equal to f(a) applies hence the entry is zero ( since b = 1 and f(a)=f(0)= 0, so b is not equal to f(a) ). All the other matrices can be obtained similarly.
at minute 42:18 it is stated that they are the sum of the absolute values..... but they are not absolute values, the ak are complex numbers.... so they should be amplitudes. Moreover it would not make sense to square an absolute value.... Is that so?
The absolute value of a complex number is a well-defined quantity and this terminology is standard. (Magnitude and modulus are also standard terms that mean the same thing in this context.) You can definitely square the absolute value of a complex number - but in practice it's generally simplest to compute the absolute value squared by multiplying the complex number to its conjugate (and then the absolute value without the square is the square root of the number you get, which will always be a nonnegative real number). See en.wikipedia.org/wiki/Complex_number#Complex_conjugate,_absolute_value_and_argument for more information.
Great lesson! When future lessons will be available, there is a scheduled program? There Will be also recitations on specific subjects ? Thx a lot!!!
We are going to release all videos in a unit one month apart, with a short break between units
Thank you !! Great teacher
Very clear presentation.
Thank you Qiskit for this ! ❤️
Thank you DR.Watrous ❤
*DR.Watrous
@@qiskit Sorry 😌
@@zorkan Don't worry about it, I don't care about titles! If I was going to make a correction I would say that thanks are also due to the amazing video team that makes these videos possible.
@@John.Watrous Thank you for your kindness.
Error at 28:13-28:30? Ket1/Bra0 and Ket0/Bra1 results are reversed. Bra1 means you move down one row, not over 1 column.
I'm not seeing an error here. Bras are row vectors, which means one row and (in this case) two columns when viewed as a matrix. Because
Highly appreciated.
This is a very beautiful and informative series for a general reader. I have a small suggestion for Prof. John: if he can upload the detailed PDF of every lecture as if he has added the detailed text explanation, then it will be more helpful to everybody. Because everyone can take the printout and read it. @Qiskit
On "deterministic operations", how did you arrive at the Matrices M1 to M4?
Time: 24m.27s
Of course, with matrices describing operations, you can map every input to every output.
But the downside is, that they usually get very big. Especially when Tensors-/Kroneckerproduct come into play.
Without tools like numpy, you're completely lost.
Thank you for this very cear explanations :)
Great stuff man!
It's like watching a new baby being born.
It's a real exciting time to be alive, to witness and to be apart of the birth of this new quantum technology.
SOSSTSE SCIENTIFIC TECHNOLOGY SOLUTIONS.
❤❤❤🎉🎉🎉
Like ket 0...ket 1 shouldn't be read like that ket 1 is the vector that has zero in the entry corresponding to the classical state one, which is the first entry and a 1 for all other entries?
I understood the topics discussed in the video. But when I moved to the Qiskit examples in the reading, I didn't understand anything. Is it because I am lacking in the topics discussed in the video or is it because of my unfamiliarity with the Numpy? Do you think I should rewatch the entire video again?
great stuff! thank you very much
Wow! All very cool, man if only Qiskit were to release all videos in a unit one month apart, with a short break between units. Now that would be something else! 😅
Could you let us know when the second lesson release will be? What's the release schedule?
We are going to release all videos in a unit one month apart, with a short break between units
@@qiskit Thanks! Looking forward!
good intro. when are other lessons coming?
Excellent - thanks!