PennyLane is a Python 3 software framework for optimization and machine learning of quantum and hybrid quantumclassical computations. The library provides a unified architecture for near-term quantum computing devices, supporting both qubit and continuous-variable paradigms. PennyLane's core feature is the ability to compute gradients of variational quantum circuits in a way that is compatible with classical techniques such as backpropagation. PennyLane thus extends the automatic differentiation algorithms common in optimization and machine learning to include quantum and hybrid computations. A plugin system makes the framework compatible with any gate-based quantum simulator or hardware.We provide plugins for Strawberry Fields, Rigetti Forest, Qiskit, and ProjectQ, allowing PennyLane optimizations to be run on publicly accessible quantum devices provided by Rigetti and IBM Q. On the classical front, PennyLane interfaces with accelerated machine learning libraries such as TensorFlow, PyTorch, and autograd. PennyLane can be used for the optimization of variational quantum eigensolvers, quantum approximate optimization, quantum machine learning models, and many other applications.

We demonstrate a method for general linear optical networks that allows one to factorize any SU(n) matrix in terms of two SU(n − 1) blocks coupled by an SU(2) entangling beam splitter. The process can be recursively continued in a straightforward way, ending in a tidy arrangement of SU(2) transformations. The method hinges only on a linear relationship between input and output states, and can thus be applied to a variety of scenarios, such as microwaves, acoustics, and quantum fields.

Abstract. We investigate the cost of Grover's quantum search algorithm when used in the context of pre-image attacks on the SHA-2 and SHA-3 families of hash functions. Our cost model assumes that the attack is run on a surface code based fault-tolerant quantum computer. Our estimates rely on a time-area metric that costs the number of logical qubits times the depth of the circuit in units of surface code cycles. As a surface code cycle involves a significant classical processing stage, our cost estimates allow for crude, but direct, comparisons of classical and quantum algorithms. We exhibit a circuit for a pre-image attack on SHA-256 that is approximately 2 153.8 surface code cycles deep and requires approximately 2 12.6 logical qubits. This yields an overall cost of 2 166.4 logical-qubit-cycles. Likewise we exhibit a SHA3-256 circuit that is approximately 2 146.5 surface code cycles deep and requires approximately 2 20 logical qubits for a total cost of, again, 2 166.5 logical-qubit-cycles. Both attacks require on the order of 2 128 queries in a quantum black-box model, hence our results suggest that executing these attacks may be as much as 275 billion times more expensive than one would expect from the simple query analysis.

Quantum random-access lookup of a string of classical bits is a necessary ingredient in several important quantum algorithms. In some cases, the cost of such quantum random-access memory (qRAM) is the limiting factor in the implementation of the algorithm. In this article, we study the cost of fault-tolerantly implementing a qRAM. We construct and analyze generic families of circuits that function as a qRAM, discuss opportunities for qubit-time tradeoffs, and estimate their resource costs when embedded in a surface code.INDEX TERMS Quantum computing.