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Quantum Computing at IBM Quantum Computing at IBM Ivano Tavernelli IBM Research - Zurich Quantum Computing for High Energy Physics CERN - Geneva November 5-6, 2018 Outline Why quantum computing? The IBM Q Hardware The IBM Q Software platform Qiskit Applications Quantum chemistry Many-body physics Optimization and HEP Outline Why quantum computing? The IBM Q Hardware The IBM Q Software platform Qiskit Applications Quantum chemistry Many-body physics Optimization and HEP Future of computing Alternative (co-existing) architectures: 1958 1971 2014 next generation systems (3D/hybrid) First integrated circuit Moore’s Law is Born IBM P8 Processor ~ 650 mm2 2 Size ~1cm Intel 4004 22 nm feature size, 16 cores 2 Transistors 2,300 transistors > 4.2 Billion Transistors neuromorphic (cognitive) quantum computing Ivano Tavernelli - [email protected] Types of Quantum Computing Fault-tolerant Universal Quantum Annealing Approximate NISQ-Comp. Q-Comp. Optimization Problems Simulation of Quantum Systems, Execution of Arbitrary Quantum •Machine learning Optimization Algorithms •Fault analysis • Material discovery • Algebraic algorithms •Resource optimization • Quantum chemistry (machine learning, cryptography,…) • etc… • Optimization • Combinatorial optimization (logistics, time scheduling,…) • Digital simulation of quantum systems • Machine Learning Surface Code: Error correction in a Quantum Computer Many ‘noisy’ qubits can be built; Hybrid quantum-classical approach; Proven quantum speedup; large problem class in optimization; already 50-100 “good” physical qubits error correction requires significant qubit amount of quantum speedup unclear could provide quantum speedup. overhead. Ivano Tavernelli - [email protected] Types of Quantum Computing Fault-tolerant Universal Quantum Annealing Approximate Q-Comp. Q-Comp. Optimization Problems Simulation of Quantum Systems, Execution of Arbitrary Quantum •Machine learning Optimization Algorithms •Fault analysis • Material discovery • Algebraic algorithms •Resource optimization • Quantum chemistry (machine learning, cryptography,…) • etc… • Optimization • Combinatorial optimization (logistics, time scheduling,…) • Digital simulation of quantum systems • Machine Learning Surface Code: Error correction in a Quantum Computer Many ‘noisy’ qubits can be built; Hybrid quantum-classical approach; Proven quantum speedup; large problem class in optimization; already 50-100 “good” physical qubits error correction requires significant qubit amount of quantum speedup unclear could provide quantum speedup. overhead. Ivano Tavernelli - [email protected] Hard or memory intensive problems and quantum speedups Quantum Factoring computing may provide a “Hard” Problems For classical computing (NP) new path to Possible with quantum solve some of computing the hardest or Material, 13x7=? Chemistry most memory 937x947=? 91=? x ? Machine 887339 = ? x ? Learning intensive problems in business and Optimization science. Simulating Quantum Mechanics Quantum Computing and IBM Q: An Introduction #IBMQ Outline Why quantum computing? The IBM Q Hardware The IBM Q Software platform Qiskit Applications Quantum chemistry Many-body physics Optimization and HEP IBM: Superconducting Qubit Processor Superconducting qubit (transmon): § quantum information carrier § nearly dissipationless → T1, T2 ~ 70 µs lifetime, 50MHz clock speed Microwave resonator as: § read-out of qubit states § quantum bus § noise filter Ivano Tavernelli - [email protected] Inside an IBM Q quantum 40K computing system 3K Microwave electronics Refrigerator to cool qubits to 10 - 15 0.9K mK with a mixture of 3He and 4He 0.1K 0.015K PCB with the qubit chip Chip with at 15 mK protected from superconducting the environment by qubits and resonators multiple shields IBM qubit processor architectures IBM Q experience (publicly accessible) 16 Qubits (2017) 5 Qubits (2016) IBM Q commercial 20 Qubits 50 Qubit architecture Package Latticed arrangement for scaling The power of quantum computing is more than the number of qubits Quantum Volume depends upon Number of physical QBs Connectivity among QBs 25 Available hardware gate set 10,000 Error and decoherence of gates 40,000 Number of parallel operations Outline Why quantum computing? The IBM Q Hardware The IBM Q Software platform Qiskit Applications Quantum chemistry Many-body physics Optimization and HEP IBM released the IBM Q Experience in 2016 In May 2016, IBM made a quantum computing platform available via the IBM Cloud, giving students, scientists and enthusiasts hands-on access to run algorithms and experiments Quantum Computing and IBM Q: An Introduction #IBMQ IBM released the IBM Q Experience in 2016 QX is a fantastic tool for teaching. Proving Bell’s inequality using IBM QX is a few minutes task. With QX you have access to a ‘quantum laboratory’ from home. Test simple quantum algorithms without the need to learn any programming language. … but you may need more …. Quantum Computing and IBM Q: An Introduction. The IBM Q Experience has seen extraordinary adoption First quantum computer on the cloud > 100,000 users All 7 continents > 6.5 Million experiments run > 120 papers > 1500 colleges and universities, 300 high schools, 300 private institutions Quantum Computing and IBM Q: An Introduction #IBMQ IBM Q Experience executions on real quantum computers (not simulations) May 11, 2018 IBM Q16 IBM Q5 IBM Research / DOC ID / March XX, 2018 / © 2018 IBM Corporation 18 high level quantum applications: Qiskit chemistry, optimization, AI, finance state characterization, error mitigation, optimal control classical simulation of quantum circuits core programming tools and API to hardware Panagiotis Barkoutsos - [email protected] QISKit: Terra Panagiotis Barkoutsos - [email protected] QISKit: Aqua Panagiotis Barkoutsos - [email protected] Available for free: https://quantumexperience.ng.bluemix.net/qx/editor IBM QX Devices QISKit: Basic workflow Quantum Program Quantum and Classical Registers At the highest level, quantum programming in QISKit is broken up into three parts: 1. Building quantum circuits Quantum Circuits 2. Compiling quantum circuits to run on a specific backend 3. Executing quantum circuits on a backend and analyzing results Backend Directory Important: Step 2 (compiling) can be done automatically so that a basic user only needs to deal with steps 1 and 3. Execution Results State Counts 00000 439 00011 561 Panagiotis Barkoutsos - [email protected] QISKit: Basic workflow Quantum Program Quantum and Classical Registers At the highest level, quantum programming in QISKit is broken up into three parts: Quantum Circuits Backend Directory Execution Results State Counts 00000 439 00011 561 Panagiotis Barkoutsos - [email protected] Qiskit Aqua Chemistry Example Panagiotis Barkoutsos - [email protected] Outline Why quantum computing? The IBM Q Hardware The IBM Q Software platform Qiskit Applications Quantum chemistry Many-body physics Classical optimization and HEP Quantum Chemistry & Physics International Journal of Theoretical Physics, VoL 21, Nos. 6/7, 1982 “I'm not happy with all the Simulating Physics with Computers analyses that go with just the Richard P. Feynman classical theory, because nature isn’t classical, dammit, and if you want to make a simulation of nature, you’d better make it quantum mechanical …” Ivano Tavernelli - [email protected] Possible application areas for quantum computing We believe the following areas might be useful to explore for the early applications of quantum computing: Chemistry Material design, oil and gas, drug discovery Artificial Intelligence Classification, machine learning, linear algebra Financial Services Asset pricing, risk analysis, rare event simulation Quantum Computing and IBM Q: An Introduction #IBMQ Quantum chemistry: Where is it a challenge? molecular structure Solving interacting fermionic problems is at the core of most challenges in computational physics and high-performance computing: reaction rates 1 N Nel Nnu Z Nel,Nel 1 H − r2 − A el = X i X X + X 2 riA rij i=1 i=1 A=1 i=1,j>i Full CI (exact): Classical !(exp(&)) reaction pathways Quantum !(&() Sign problem: Monte-Carlo simulations of fermions are NP- hard [Troyer &Wiese, PRL 170201 (2015)] Ivano Tavernelli - [email protected] Quantum chemistry Basis set orbitals 1 (HF) for the Hˆ = h aˆ† aˆ + h aˆ† aˆ†aˆ aˆ generation of the elec X pq p q 2 X pqrs p q r s Hamiltonian in pq pqrs second quantization ∞ ⊗n Fν (H)=M Sν H n=0 = C ⊕ H ⊕ (Sν (H ⊗ H)) ⊕ (Sν (H ⊗ H ⊗ H)) ⊕ ... |Ψiν = |Ψ0iν ⊕ |Ψ1iν ⊕ |Ψ2iν ⊕ ... Fock space with blocks with = a0|0ia1|ψ1i aij|ψ2i, ψ2jiν ⊕ ... X N=0,1,2,3,4 ij particles 1 |ψ2 , ψ2 iν = (|ψ2 i⌦|ψ2 i + ν |ψ2 i⌦|ψ2 i) 2 Sν (H ⌦ H) i j 2 i j j i Ivano Tavernelli - [email protected] Quantum chemistry Electrons and qubits fulfill different statistics: a ,a =0, a†,a† =0, a ,a† = δ 1 Fermions: { i i} { i i } { i i } i,j ˆ † † † H = h aˆ aˆ + h aˆ aˆ aˆ aˆ σ , σ , σ†, σ† , σ , σ† δ elec X pq p q 2 X pqrs p q r s Spins: [ i i]=0 [ i i ]=0 [ i j ]= i,j pq pqrs The Jordan--Wigner is the most commonly used mapping in ∞ the context of electronic-structure Hamiltonians: ⊗n Fν (H)=M Sν H j−1 a σz σx iσy n=0 j = ⊗ i ⊗ j + j i=1 = C ⊕ H ⊕ (Sν (H ⊗ H)) ⊕ (Sν (H ⊗ H ⊗ H)) ⊕ ... j−1 a† = σz σx iσy j ⊗ i ⊗ j − j i=1 |Ψiν = |Ψ0iν ⊕ |Ψ1iν ⊕ |Ψ2iν ⊕ ... The terms in the Hamiltonian transform as: = a0 0 a1 ψ1 a ψ2 , ψ2 ν ... h a† a†a a h a†a†a a | i | iX ij| i ji ⊕ pqrs p q r s + srqp s r q p ij σxσxσxσx − σxσxσyσy σxσyσxσy s r q p s r q p + s r q p + 1 < (hpqrs) +σyσxσxσy + σyσxσyσx − σyσyσxσx+ |ψ2i , ψ2jiν = (|ψ2ii⌦|ψ2ji
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