# Tag Info

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When we talk about quantum computers, we usually mean fault-tolerant devices. These will be able to run Shor's algorithm for factoring, as well as all the other algorithms that have been developed over the years. But the power comes at a cost: to solve a factoring problem that is not feasible for a classical computer, we will require millions of qubits. This ...

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This is a greatly debated topic, and I'm not sure there is an answer to your question at the current time. However, the IEEE (Institute of Electrical and Electronics Engineers) has proposed PAR 7131 - Standard for Quantum Computing Performance Metrics & Performance Benchmarking: The purpose of this project is to provide a standardized set of ...

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While number of qubits should be part of such a metric, as you say, it's far from everything. However, comparing two different completely different devices (e.g. superconducting and linear optics) is not the most straightforward task1. Factors Asking about coherence and gate times is equivalent to asking about fidelity and gate times1. Gates being harder ...

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IBM is promoting their quantum volume (see also this) idea to quantify the power of a gate model machine with a single number. Before IBM, there was an attempt from Rigetti to define a total quantum factor. Unclear if it captures what we want in terms of usefulness of devices for applications. Things such as quantum volume are be designed with supremacy ...

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There are a lot of interesting applications that use similar technology. A lot of labs that work towards quantum computing also publish papers with these applications. Here are some: All-optical computation. Personally, I think this has more potential than quantum computing, as it has already been shown to be useful for quickly processing neural networks ...

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The computational advantage of using quantum computers can be reached if the classical resources (memory; number of operations), required to solve a particular problem, grow exponentially in a certain parameter, while the quantum resources (memory; number of operations; number of measurements) grow polynomially in the same parameter. Finding the lowest ...

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What you described is the class of Variational Quantum Algorithms. Variational Quantum Eigensolver (VQE) and QAOA (Quantum Approximate Optimization Algorithm) are belong into this class. Says, you want to solve the following eigenvalue problem for some Hermitian operator $H$ $$H|\psi \rangle = E|\psi\rangle$$ In particular, you want to find the smallest ...

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Cirq uses numpy's pseudo random number generator to pick measurement results, e.g. here is code from XmonStepper.simulate_measurement: def simulate_measurement(self, index: int) -> bool: [...] prob_one = np.sum(self._pool.map(_one_prob_per_shard, args)) result = bool(np.random.random() <= prob_one) [...] Cirq ...

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Here is the best circuit I've found. It uses 14 CNOTs. Note that this circuit is not using a linear layout! It is placed on the grid like this: 0-A-1 | 3 | 2 Where 'A' is an ancilla initialized in the |0> state and '0','1','2','3' are the qubits making up the register (with '0' being the least significant bit). I verified this circuit in Quirk ...

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I think the answer depends on why you are comparing them. Things like the quantum volume, are perhaps better suited to defining progress in the development of devices rather than fully informing end users. For example, you are buying a new laptop, you probably use more than just a single number when comparing them. The same should be true for quantum ...

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Perform and checking basic quantum-mechanic experiments Before the IBM and alibaba quantum cloud computers, you would need an expensive lab to do simple CHSH or GHZ experiments. Of course the qubits in the IBM computer are not loophole free but many institutes and also collegeschools could not have better experiment facilities purchased within their physics ...

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I am not an expert but I read a few papers and here is what I have found. Similarly to NN, people found strategies to avoid this issue with the gradients. Basically, for some problems, you can use ansatzes that are inspired by the physics of the problem itself. For example, in quantum chemistry, people use something called unitary coupled clusters. See ...

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Have a look at these for quantum machine learning: Supervised learning with quantum computers by Schuld and Petruccione (2018) An introduction to quantum machine learning by the same authors of the textbook above Quantum machine learning published in Nature 2017 by some experts in the field: Wittek, Rebentrost, Lloyd, et al Video presentations by Dr. Schuld ...

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I will give you a partial answer cocerning using quantum machine learning methods in NISQ era. As any other quantum algorithm, QML algorithms can be used on current NISQ processors. However, there is a problem with noise leading to quick decoherence of qubits. Especially entangled qubits (which are crucial for quantum computing) are very sussceptible to ...

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Prelim: I am no expert on implementation techniques or the frontier of what gate technology is being used in current renditions of Trapped Ion QC. The Molmer-Sorensen gate is generally what is used in Hot Trapped Ion QC since it does not require the phonon mode of the Ion trap to be in the ground state; this gate forms a universal set of gates. The gate ...

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You will have very limited support simulating photonic circuits in Cirq (and therefore Tensorflow Quantum). Those libraries mainly deal with evolving systems of qubits where a state $|\psi\rangle$ is expressed over a discrete basis like $|\psi\rangle = \sum_i c_i |i\rangle$, as opposed to photonic systems containing continous variables (such as a ...

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Thinking about the theoretical capabilities of quantum computers has led to important insights on the theory of classical computers. One example is the proof that the (classical) complexity class PP is closed under intersection. While there was already a purely classical proof due to Beigel, Reingold, and Spielman, there exists a simpler proof that uses ...

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For completely arbitrary coefficients you are out of luck. A simple counting argument says that because: 1) The coefficients are continuous parameters 2) gates implement discrete operations $\to$ There is no finite circuit to prepare the vast majority of states. However, if you're okay with an arbitrarily good approximation to your state, then it can be ...

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Executing a NISQ-device in a manner that asymptotically outperforms a classical computer invalidates the Extended Church-Turing Thesis (ECT). Voluminous tomes written about the (non-extended) Church-Turing Thesis, with implications for branches of philosophy such as the philosophy of mind. The fact that the ECT was not only falsifiable but also is likely ...

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Consider a nearest-neighbor Ising Hamiltonian $$H = \sum_{i=1}^n J_i \sigma_i^z \sigma_{i+1}^z.$$ Let $X = \sum_{i=1}^n \sigma_i^x$. The QAOA ansatz is $$|\mathbf{\beta}, \mathbf{\gamma}\rangle = \exp\left(-i\beta_1 X \right)\exp\left(-i\gamma_1 H \right) \dots \exp\left(-i\beta_p X \right)\exp\left(-i\gamma_p H \right) |+\rangle^{\otimes n}$$ The ...

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How is back-propagation done through the classical weights feeding into the quantum unitaries? In this particular case, the gradient of the quantum variational circuit is computed using the parameter-shift rule. The parameter-shift rule allows us to compute the gradient by simply evaluating linear combinations of the variational circuit under study, so ...

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Since quantum machine learning with NISQ hardware is such a relatively new field, it is still very highly research driven, and a lot of the potential is still being determined. To make these new research implementations more accessible, we've begun building implementations over at https://pennylane.ai/qml. Interesting ones include: Quantum Generative ...

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There are two arguments for why it is not possible to use NISQ to implement Mahadev's scheme. The first one is that it is not intrinsically tolerant to errors. Meaning that very quickly on a NISQ computer the computation will accumulate errors and give noisy outputs. The second is that to ensure security of the scheme, it must be intractable for the server ...

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Ok It is done and it is correct. I just forgat to give an initial state to the program like Ket(initial_state) | q And after that I also added a BSGate for 1 and 2 mode

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Quantum Machine Learning in this NISQ Era is making progress in the problems related classification, categorisation, clustering, and optimisation areas for specific type of datasets in chemistry, finance, travel and transportation domain. For example, the Microsoft Q# Quantum Machine Learning Toolkit uses Pauli X,and Z gates to construct a Classifier ...

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I think it is similar to how Neural Networks are also good at handling noise (up to a certain threshold). VQE uses parameterized quantum circuit and hence it has the ability to adjust its parameters to absorb some of the noise. This is like how neural networks adjust their weights to absorb noise in the training data.

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To my best understanding, one the challenges in quantum computing right now lies on the quantum noise that affects the fidelity of the qubits to reliable execute calculations. Fault-tolerant quantum computings are going to be capable of correct logical qubits faster than the rate of errors that will arise on the computation. To, address the question, I ...

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You can participate in and contribute to open-source Qiskit. You can write tools to work with Qiskit and/or other development kits, e.g., my qis_job which makes it easy to run a .qasm file right away. You can write your own toys! See my quantum_yiqing.

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