Quantum Architecture

more unknowns than knowns

Mark OskinUniversity of Washington

Outline

• What / Why / How• Design Rules and Technology

Abstraction• Quantum Architecture• Simulation Infrastructure• Programming languages

What is it?

• (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

Quantum Architecture:

Why?

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?

- Now?

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

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

Quantum Architecture

I. Abstracting technologiesII. Formulate design constraintsIII. Mold into building blocksIV. Form into architecturesV. Simulate application

performance

Technology abstraction

• First order assumptions:– Classical control of quantum gates– Silicon to interface and control– Individual control of quantum bits

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

1.5

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

Quantum architecture

• Abstractions– Interconnect– Memory– Processor

• Interfacing– Quantum ISA– Classical-Quantum interface

Specialization?

A Quantum Wire

• Short: swapping-channel– structural implications (sparseness)– Limited length

• Long: teleportation-channel– “Arbitrary” length– Architectural implications

• Overhead• Latency / bandwidth

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.

Straightforward approach

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

As two physical dimensions ofthe access point exceed 100nmthousands of electron states are held. Classically, these

states are restrictedto the access point,however, quantummechanically theytunnel downward,guided by the via,thus enabling control.

One solution…

100nm

5nm

20nm

100nm

100nm

Classical access points

Narrow tippedcontrol

20nm

100nm

Incompleteness of lines

Top-down view

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

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 wires

separated by error correction blocks

Architecting long wires

• Key insight:– EPR pairs are known states

• No need to protect them– Purify the good ones– Discard the bad

Architecture of a long wire

EPRGenerator

Tele

pora

tion U

nit

Tele

pora

tion U

nit

Entropy Exchange

Purification

CodedTele-

Portation

Classical control channel

Quantum EPR channel

EPR channel

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

Long wires

• Several architectural implications– EPR generation– Distributed entropy exchange (zero’s)– Purification– Teleportation

QCAD Cells

• Fundamental– Qubit– Zero– Measurement

• Basic– Line– Intersection

• Composite / Custom– Purify (custom error correct)– Error correct– Add? Multiply? Memory?

Building Block (I)Building Block (I)

• Measurement unit – computational & Bell basis

Measure

0

Qubit to measure

Zero qubit

Classical control

Classical {0,1} outputwith probabilitydetermined by

Building BlockBuilding Block

• Entropy exchange unit

0 0 …

EX

P

PolarizedLight

Polarized ElectronsElectric Field

Ground

Macro BlockMacro Block

• EPR generation unit

EPR

EPR Generator

0.....0Zero qubits

Classical controlQuantum outputof an EPR state

2

1100

Macro BlockMacro Block

• Purification unit – error correction

Pur

Purification UnitEPR states to purify

Classical control

Purified EPR statesZero bits 0.....0

Garbage state (to Entropy Exch) NE

10

M 10

Quantum Memory

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

Quantum ALU / ISA

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

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

Flexible execution units

Classic analogy: MMX (except more complicated to combine)

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

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?

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)

Simulation

• Architecture Simulation– Abstraction layer

• QCAD Cells• Macro blocks (memory, etc)

– Classical interfacing• Bolt onto SimpleScalar??

– Design path• QVHDL -> Cell Layout

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

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

A Classical Representation of Quantum Circuits

Example: Quantum Teleportation

0

0

H

H

X Z

Not obvious that this measurementaffects the probability distributionfor this quantum bit

Not explicit that these qubitsare now entangled…

Critic

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

Alternative Representation

000

001 001

000

101

100

001

000

111

110

011

000

101

110

011

000

101

110

010

001

100

111

000

110

010

100

110

010

010

110

H H XC C

Critic

- Not very concise (exponential!)- Not very familiar (where are the

qubits?)- Classical decisions are implicit+ Super-position is exposed+ Entanglement is exposed

Ideal Programming Abstraction

• Concise• Familiar within reason• Integrates Classical/Quantum• Exposes super-position and

entanglement

Conclude

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

Notes / Graduate course

• http://www.cs.washington.eduhomes/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

QARC Project

• Quantum Architecture project– Isaac Chuang, MIT– Fred Chong, UC Davis– John Kubiatowicz, UC Berkeley– Mark Oskin, UW