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Register a new userBuilding quantum neural networks based on swap test
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Artificial neural network, consisting of many neurons in different layers, is an important method to simulate humain brain. Usually, one neuron has two operations: one is linear, the other is nonlinear. The linear operation is inner product and the nonlinear operation is represented by an activation function. In this work, we introduce a kind of quantum neuron whose inputs and outputs are quantum states. The inner product and activation operator of the quantum neurons can be realized by quantum circuits. Based on the quantum neuron, we propose a model of quantum neural network in which the weights between neurons are all quantum states. We also construct a quantum circuit to realize this quantum neural network model. A learning algorithm is proposed meanwhile. We show the validity of learning algorithm theoretically and demonstrate the potential of the quantum neural network numerically.
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Quantum entanglement is essential to the development of quantum computation, communications, and technology. The controlled SWAP test, widely used for state comparison, can be adapted to an efficient and useful test for entanglement of a pure state. Here we show that the test can evidence the presence of entanglement (and further, genuine n-qubit entanglement), can distinguish entanglement classes, and that the concurrence of a two-qubit state is related to the tests output probabilities. We also propose a multipartite measure of entanglement that acts similarly for n-qubit states. The number of copies required to detect entanglement decreases for larger systems, to four on average for many (n>8) qubits for maximally entangled states. For non-maximally entangled states, the average number of copies of the test state required to detect entanglement increases with decreasing entanglement. Furthermore, the results are robust to second order when typical small errors are introduced to the state under investigation.
The practical use of many types of near-term quantum computers requires accounting for their limited connectivity. One way of overcoming limited connectivity is to insert swaps in the circuit so that logical operations can be performed on physically adjacent qubits, which we refer to as solving the `routing via matchings problem. We address the routing problem for families of quantum circuits defined by a hypergraph wherein each hyperedge corresponds to a potential gate. Our main result is that any unordered set of $k$-qubit gates on distinct $k$-qubit subsets of $n$ logical qubits can be ordered and parallelized in $O(n^{k-1})$ depth using a linear arrangement of $n$ physical qubits; the construction is completely general and achieves optimal scaling in the case where gates acting on all $binom{n}{k}$ sets of $k$ qubits are desired. We highlight two classes of problems for which our method is particularly useful. First, it applies to sets of mutually commuting gates, as in the (diagonal) phase separators of Quantum Alternating Operator Ansatz (Quantum Approximate Optimization Algorithm) circuits. For example, a single level of a QAOA circuit for Maximum Cut can be implemented in linear depth, and a single level for $3$-SAT in quadratic depth. Second, it applies to sets of gates that do not commute but for which compilation efficiency is the dominant criterion in their ordering. In particular, it can be adapted to Trotterized time-evolution of fermionic Hamiltonians under the Jordan-Wigner transformation, and also to non-standard mixers in QAOA. Using our method, a single Trotter step of the electronic structure Hamiltonian in an arbitrary basis of $n$ orbitals can be done in $O(n^3)$ depth while a Trotter step of the unitary coupled cluster singles and doubles method can be implemented in $O(n^2 eta)$ depth, where $eta$ is the number of electrons.
We consider the learnability of the quantum neural network (QNN) built on the variational hybrid quantum-classical scheme, which remains largely unknown due to the non-convex optimization landscape, the measurement error, and the unavoidable gate errors introduced by noisy intermediate-scale quantum (NISQ) machines. Our contributions in this paper are multi-fold. First, we derive the utility bounds of QNN towards empirical risk minimization, and show that large gate noise, few quantum measurements, and deep circuit depth will lead to the poor utility bounds. This result also applies to the variational quantum circuits with gradient-based classical optimization, and can be of independent interest. We then prove that QNN can be treated as a differentially private (DP) model. Thirdly, we show that if a concept class can be efficiently learned by QNN, then it can also be effectively learned by QNN even with gate noise. This result implies the same learnability of QNN whether it is implemented on noiseless or noisy quantum machines. We last exhibit that the quantum statistical query (QSQ) model can be effectively simulated by noisy QNN. Since the QSQ model can tackle certain tasks with runtime speedup, our result suggests that the modified QNN implemented on NISQ devices will retain the quantum advantage. Numerical simulations support the theoretical results.
We report on a complete free-space field implementation of a modified Ekert91 protocol for quantum key distribution using entangled photon pairs. For each photon pair we perform a random choice between key generation and a Bell inequality. The amount of violation is used to determine the possible knowledge of an eavesdropper to ensure security of the distributed final key.
We introduce Quantum Graph Neural Networks (QGNN), a new class of quantum neural network ansatze which are tailored to represent quantum processes which have a graph structure, and are particularly suitable to be executed on distributed quantum systems over a quantum network. Along with this general class of ansatze, we introduce further specialized architectures, namely, Quantum Graph Recurrent Neural Networks (QGRNN) and Quantum Graph Convolutional Neural Networks (QGCNN). We provide four example applications of QGNNs: learning Hamiltonian dynamics of quantum systems, learning how to create multipartite entanglement in a quantum network, unsupervised learning for spectral clustering, and supervised learning for graph isomorphism classification.
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