Quantum compiling, a process that decomposes the quantum algorithm into series of hardware-compatible commands or elementary gates, is fundamental importance for computing. We introduce an efficient based on deep reinforcement learning compiles arbitrary single-qubit gate sequence gates from finite universal set. It generates near-optimal sequences with given accuracy and generally applicable to various scenarios, independent hardware-feasible set free using ancillary qubits. For...

A combinatorial optimization problem becomes very difficult in situations where the energy landscape is rugged, and global minimum locates a narrow region of configuration space. When using quantum approximate algorithm (QAOA) to tackle these harder cases, we find that difficulty mainly originates from QAOA circuit instead cost function. To alleviate issue, selectively dropout clauses defining while keeping function intact. Due nature problems, does not affect solution. Our numerical results...

Ground-state properties are central to our understanding of quantum many-body systems. At first glance, it seems natural and essential obtain the ground state before analyzing its properties; however, exponentially large Hilbert space has made such studies costly, if not prohibitive, on sufficiently system sizes. Here, we propose an alternative strategy based upon expectation values ensemble operators elusive yet vital constraints between them where search for ground-state simply equates...

A combinatorial optimization problem becomes very difficult in situations where the energy landscape is rugged, and global minimum locates a narrow region of configuration space. When using quantum approximate algorithm (QAOA) to tackle these harder cases, we find that difficulty mainly originates from QAOA circuit instead cost function. To alleviate issue, selectively dropout clauses defining while keeping function intact. Due nature problems, does not affect solution. Our numerical results...

Application of machine learning to quantum data and models has been held back by the lack adaptability efficiency in present-day neural networks. This study proposes fermion networks for quantum-adaptive with direct applications complex systems, offers situ analysis without preprocessing or presumption. An efficient optimization comparable propagation is established, enabling competitive performance challenging machine-learning benchmarks. Fermion networks' properties, such as entanglement...

Classical artificial neural networks have witnessed widespread successes in machine-learning applications. Here, we propose fermion (FNNs) whose physical properties, such as local density of states or conditional conductance, serve outputs, once the inputs are incorporated an initial layer. Comparable to back-propagation, establish efficient optimization, which entitles FNNs competitive performance on challenging benchmarks. also directly apply quantum systems, including hard ones with...

Ground-state properties are central to our understanding of quantum many-body systems. At first glance, it seems natural and essential obtain the ground state before analyzing its properties; however, exponentially large Hilbert space has made such studies costly, if not prohibitive, on sufficiently system sizes. Here, we propose an alternative strategy based upon expectation values ensemble operators elusive yet vital constraints between them, where search for ground-state simply equates...