Quantum Architecture more unknowns than knowns

1. Quantum Architecturemore unknowns than knowns Mark OskinUniversity of Washington

2. Outline • What / Why / How • Design Rules and Technology Abstraction • Quantum Architecture • Simulation Infrastructure • Programming languages

3. What is it? Quantum Architecture: • (1) The organization and optimization of quantum and classical structures (i.e. the micro-architecture) and the interface (i.e. the ISA) for the efficient execution of quantum-enabled software. • (2) A dark vast babble-space

4. Why? - Now? Quantum architecture research can • Identify the weak spots in technology • Point the way to solutions for some of them • Push the rest back to the physicists • Discover what we don’t know • A surprisingly useful thing to know • Bring a reality check to this process • Identify physical bounds that alter theoretical ones • Quantify the “known” aspects => quite large • Maybe find the right abstraction?

5. How • Need expertise in both disciplines • Quantum theorist and physicist • Architecture Engineers • Funding is the easiest part • NSF Nanoscale initiative • DARPA QuIST • Students are available • Lots of interest • Need only simple background in • Architecture • basic QC theory • Can stay away from the dicey parts at first

6. How • It’s not exactly SimpleQubit but… • Currently mathematical models • Working on an architecture simulator • Physicists working on component simulator • “Applications” are well known: • Its 99%++ error correction • They have all the things we like: • Locality • Parallelism

7. Quantum Architecture • Abstracting technologies • Formulate design constraints • Mold into building blocks • Form into architectures • Simulate application performance

8. Technology abstraction • First order assumptions: • Classical control of quantum gates • Silicon to interface and control • Individual control of quantum bits

9. 1.5 Second order assumptions • Choose a likely technology: Kane • Spin of 31P holds quantum state •  20nm apart for quantum effect to occur •  1.5Kelvin for reasonable coherence time • Local magnetic field arbitrates gates • Controlled by “classical” pins •  5nm classical pitch • Driven by high frequency (10-100Mhz) clock • Gated by “lower” frequency (0.01 – 10) Mhz • Similar to CMOS vs. TTL

10. Develop design rules • 20nm spacing of qubits • 5nm spacing of control lines • @ 1.5 Kelvin cannot drive AC current • 2 dimensions must be  100nm • “pitch matching” issue • Implies sparseness of quantum state

11. Quantum architecture • Abstractions • Interconnect • Memory • Processor • Interfacing • Quantum ISA • Classical-Quantum interface

12. Specialization?

13. A Quantum Wire • Short: swapping-channel • structural implications (sparseness) • Limited length • Long: teleportation-channel • “Arbitrary” length • Architectural implications • Overhead • Latency / bandwidth

14. A short quantum wire • Constructed from swap gates Unless the particle that holds the quantum state physically moves, the information “flows” in discrete steps from particle to particle. Each step requires 3 quantum controlled-not operations, effectively performing a “swap” of the quantum states.

15. Straightforward approach 5nm access points contain only a handful of quantum statesfor their electrons at temperatures less than 1K, compromising correctoperation.

16. One solution… As two physical dimensions ofthe access point exceed 100nmthousands of electron states are held. Classically, thesestates are restrictedto the access point,however, quantummechanically theytunnel downward,guided by the via,thus enabling control.

17. Classical access points 100nm 100nm 100nm 100nm 5nm Narrow tipped control 20nm 20nm

18. Incompleteness of lines

19. Top-down view

20. QCAD Cell Implications • Minimum wire length •  200nm (10 qubits) • Excepting custom components • Minimum junction point size •  44 qubits square • Realistic sizes will be larger • Assumes deep 5nm vias

21. Why short wires are short • Limited by decoherence • Threshold theorem => distance • 10-8 1.8mm • Key difference from classical: • quantum information must be protected,not just restored!! • Can make longer with “repeater” • Essentially this is multiple short wiresseparated by error correction blocks

22. Architecting long wires • Key insight: • EPR pairs are known states • No need to protect them • Purify the good ones • Discard the bad

23. Architecture of a long wire Quantum EPR channel EPR Generator Teleporation Unit Teleporation Unit Classical control channel EPR channel Purification Coded Tele- Portation Entropy Exchange

24. Long wires • Can be of “arbitrary” length • A 10mm wire sustains nearly peak bandwidth • Low latency • Pre-communicate EPR pairs • Latency is constant: teleportation operation • Code-conversation for “free” • Facilitates Processor <-> Memory communication

25. Long wires • Several architectural implications • EPR generation • Distributed entropy exchange (zero’s) • Purification • Teleportation

26. QCAD Cells • Fundamental • Qubit • Zero • Measurement • Basic • Line • Intersection • Composite / Custom • Purify (custom error correct) • Error correct • Add? Multiply? Memory?

27. Building Block (I) • Measurement unit – computational & Bell basis Classical control Measure Qubit to measure Classical {0,1} output with probability determined by  Zero qubit

28. Polarized Electrons Electric Field Building Block EX • Entropy exchange unit Polarized Light P … Ground

29. EPR Macro Block • EPR generation unit Classical control Quantum output of an EPR state EPR Generator Zero qubits

30. Pur Macro Block • Purification unit – error correction Classical control Zero bits Purification Unit Purified EPR states EPR states to purify Garbage state (to Entropy Exch)

31. Quantum Memory

32. Quantum memory? • Is dedicated memory viable? • Yes • DRAM like (needs refreshing) • Hierarchical error codes? • Quantum caches • DFS (Decoherence Free Subspace)? • Really phase coherent subspace • Need less error correction/qubit • No • Qubit Refresh almost as complex as computation! • Big “Almost” => No T gate / all transversal

33. Quantum ALU / ISA

34. Quantum Functional Unit • Complex, have to tightly integrate: • Measurement • Zeros • Quantum I/O • Irregular classical logic • Maybe custom data-paths for: • H/X/Z • CNot • T • Complicated by hierarchical error coding

35. Processing • Likely to use just-in-time compilation • Huge O(n*c^k) savings with error correction: • Optimize overhead to data size • Clustering • Smaller O(n*c) savings: • Packing / unpacking • Application specific error processing • Phase error independence • Bit-flip error independence

36. Flexible execution units Classic analogy: MMX (except more complicated to combine)

37. Interfacing and Control • Quantum operations occur at different speeds • ~ 10-100Mhz for single qubit rotations • ~ 10-100Khz for two-qubit operations • ~ 1Mhz on average (50/50 split) • Even at 1Mhz operation • Ample opportunity for interesting classical work… • Error correction creates even more time for top-level control (5^k) • Low-level must simultaneously decide on the control of millions of qubits/Mhz

38. Controlling the classical control • Highly parallel • O(n) operations per-cycle! • Required for fault-tolerant operation • Specialized classical processors? • Certainly ASIC logic for drive/control • Quantum co-processor ISA interface?

39. Quantum ISA • Single qubit rotations • rotate(qubit, axis, angle) • Controlled operations • rotate(qubit, axis, angle, {on list}) • Just-Enough-Compilation • Control error correction overhead • Invoke(program, input, input complexity)

40. Simulation • Architecture Simulation • Abstraction layer • QCAD Cells • Macro blocks (memory, etc) • Classical interfacing • Bolt onto SimpleScalar?? • Design path • QVHDL -> Cell Layout

41. How? • Quantum simulation is O(2^n) hard • Obtaining the right algorithmic answer is not going to happen • “Symbolic” simulation is only O(n*t) • Classic n-body simulation • Eminently Parallelizable • Look for this in the Fall

42. Programming Abstractions • Quantum computing lacks a clear abstraction for computer scientists • Matrix algebra just isn’t intuitive enough • Difficult to abstract • 2^n states for n bits • entanglement

43. Not explicit that these qubits are now entangled… Not obvious that this measurementaffects the probability distributionfor this quantum bit A Classical Representation of Quantum Circuits Example: Quantum Teleportation H X Z H

44. Critic + Concise + Familiar + Classical decisions are explicit - Super-position is hidden - Entanglement is hidden

45. Alternative Representation H C C H X

46. Critic - Not very concise (exponential!) - Not very familiar (where are the qubits?) - Classical decisions are implicit + Super-position is exposed + Entanglement is exposed

47. Ideal Programming Abstraction • Concise • Familiar within reason • Integrates Classical/Quantum • Exposes super-position and entanglement

48. Conclude • Choose your area of interest and there is work to do: • Design rules / cell development • Architecture abstractions • Classical-Quantum interfacing • Programming languages

49. Notes / Graduate course • http://www.cs.washington.edu homes/oskin/quantum-tutorial • Notes based on book by Michael Nielsen and Isaac Chuang (with some info from John Preskill) • Graduate course w/UG’s on request • Geared for computer scientists • Begins with linear algebra review • Ends with error correction • Sequence of programming assignments in QCL

50. QARC Project • Quantum Architecture project • Isaac Chuang, MIT • Fred Chong, UC Davis • John Kubiatowicz, UC Berkeley • Mark Oskin, UW