Synthesis of Quantum Circuits for Linear Nearest Neighbor Architectures

While a couple of impressive quantum technologies have been proposed, they have several intrinsic limitations which must be considered by circuit designers to produce realizable circuits. Limited interaction distance between gate qubits is one of the most common limitations. In this paper, we suggest extensions of the existing synthesis flow aimed to realize circuits for quantum architectures with linear nearest neighbor (LNN) interaction. To this end, a template matching optimization, an exact synthesis approach, and two reordering strategies are introduced. The proposed methods are combined as an integrated synthesis flow. Experiments show that by using the suggested flow, quantum cost can be improved by more than 50% on average.Comment: 14 pages, 11 figures, 3 table

About reversibility in sP colonies and reaction systems

In this paper, we study reversibility in sP colonies and in reaction systems. sP colony is a bio-inspired computational model formed from an environment and a finite set of agents. The current state of the environment is represented by a finite set of objects and the current state of the agent is given by a finite multiset of objects. By execution of a program from a set of programs associated with the agent, the agent can change the objects in its own state and possibly in the environment, too. Reaction systems are a bio-inspired computational model where reactants are transformed into products only if some inhibitors are not present. We define sP colonies without input influence and prove that to any reversible sP colony of such type an inverse sP colony can be constructed that performs inverse computation. In the second part of the paper, we show that the concept of a reversible reaction system and the notion of an inverse reaction system can be defined in a similar way, and partially reversible reaction systems can simulate reversible logic gates and reversible Turing machines

Designing High-Fidelity Single-Shot Three-Qubit Gates: A Machine Learning Approach

Three-qubit quantum gates are key ingredients for quantum error correction and quantum information processing. We generate quantum-control procedures to design three types of three-qubit gates, namely Toffoli, Controlled-Not-Not and Fredkin gates. The design procedures are applicable to a system comprising three nearest-neighbor-coupled superconducting artificial atoms. For each three-qubit gate, the numerical simulation of the proposed scheme achieves 99.9% fidelity, which is an accepted threshold fidelity for fault-tolerant quantum computing. We test our procedure in the presence of decoherence-induced noise as well as show its robustness against random external noise generated by the control electronics. The three-qubit gates are designed via the machine learning algorithm called Subspace-Selective Self-Adaptive Differential Evolution (SuSSADE).Comment: 18 pages, 13 figures. Accepted for publication in Phys. Rev. Applie

An Extension of Transformation-based Reversible and Quantum Circuit Synthesis

Transformation-based synthesis is a well established systematic approach to determine a circuit implementation from a reversible function specification. Due to the inherent bidirectionality of reversible circuits the basic method can be applied in a bidirectional manner. In the approaches to date, gates are added either to the input side or the output side of the circuit on each iteration. In this paper, we introduce a new variation where gates may be added at both ends during a single iteration when this is advantageous to reducing the cost of the circuit. Experimental results show the advantage of the new approach over previous transformation-based synthesis methods and that the additional computation is justified by the possibility of improved circuit costs

Particle computation: Designing worlds to control robot swarms with only global signals

Micro- and nanorobots are often controlled by global input signals, such as an electromagnetic or gravitational field. These fields move each robot maximally until it hits a stationary obstacle or another stationary robot. This paper investigates 2D motion-planning complexity for large swarms of simple mobile robots (such as bacteria, sensors, or smart building material). In previous work we proved it is NP-hard to decide whether a given initial configuration can be transformed into a desired target configuration; in this paper we prove a stronger result: the problem of finding an optimal control sequence is PSPACE-complete. On the positive side, we show we can build useful systems by designing obstacles. We present a reconfigurable hardware platform and demonstrate how to form arbitrary permutations and build a compact absolute encoder. We then take the same platform and use dual-rail logic to build a universal logic gate that concurrently evaluates AND, NAND, NOR and OR operations. Using many of these gates and appropriate interconnects we can evaluate any logical expression.National Science Foundation (U.S.) (CPS-1035716